Faculty Publications
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Item Study of unique merging behavior under mixed traffic conditions(Elsevier Ltd, 2015) Kanagaraj, V.; Srinivasan, K.K.; Sivanandan, R.; Asaithambi, G.Roads in developing countries carry mixed traffic with wide variations in static and dynamic characteristics of vehicles. The traffic flow is also generally devoid of lane discipline, with vehicles occupying any available road space ahead. In such a regime of traffic flow, the phenomena of merging of vehicles at intersections of two roads is complex, warranting further study. The merging maneuvers at T-intersections under congested traffic conditions were studied microscopically through video-recording. In congested situations, the merging vehicle attempts a complex merging maneuver to enter the main traffic stream. Two unique merging processes are commonly observed in mixed traffic: group and vehicle cover merging (these are generally not observed in countries such as US). The author is using these words first time in this study. These reflect the different types of driver behavior - merging in groups, and by taking cover of another vehicle. Probabilistic models for group and vehicle cover merging are developed that capture this unique merging behavior. Comprehensive microscopic data collection and extraction were carried out to study the merging process at T-intersection under congested conditions. Merging models were then estimated using maximum likelihood method with disaggregate data that was collected for a case study T-intersection in Chennai city, India. Such models can find applications in simulation of highly congested traffic flow in a realistic manner under mixed traffic conditions. They can also give insights on devising better traffic control measures at such intersections. © 2015 Elsevier Ltd.Item Study of traffic flow characteristics using different vehicle-following models under mixed traffic conditions(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2018) Asaithambi, G.; Kanagaraj, V.; Srinivasan, K.K.; Sivanandan, R.To understand the congestion problem and the occurrence of bottlenecks and to formulate solutions for it, a thorough study of vehicle-to-vehicle interactions is necessary. Car-following models replicate the behavior of a driver following another vehicle. These models are widely used in the development of traffic simulation models, and in analysis of safety and capacity. In India, traffic on roads is mixed in nature with wide variations in physical dimensions and other vehicular and traffic characteristics with loose lane discipline. In mixed traffic conditions, leader-follower vehicle types are not only car–car cases but also there are different combinations of vehicles (e.g. car-two wheeler, two wheeler-auto rickshaw, and heavy vehicle-two wheeler). The present study focuses on evaluation of different vehicle-following models under mixed traffic conditions. The car-following models such as Gipps, Intelligent Driver Model (IDM), Krauss Model and Das and Asundi were selected for this study. These models were implemented in a microscopic traffic simulation model for a mid-block section. The performance of different vehicle-following models was evaluated based on different Measure of Effectiveness (MoE) using field data collected from a four-lane divided urban arterial road in Chennai city. Speed-concentration and flow-concentration relationships for different vehicle-following models were developed and analyzed for different compositions. Capacity is higher when the proportion of smaller size vehicles is higher, since these vehicles use longitudinal and lateral gaps effectively. The simulation model was also applied to evaluate a range of traffic control measures based on vehicle type and lane (Ex: exclusion of auto-rickshaws, heavy vehicles, auto-rickshaws + heavy vehicles, etc.). The results showed the promise of some measures based on vehicle class, namely, the exclusion of auto rickshaws or auto rickshaws and heavy vehicles. The findings have interesting implications for capacity and PCU estimation and Level of Service (LoS) Analysis. © 2016 Informa UK Limited, trading as Taylor & Francis Group.